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Kubernetes & OpenShift Financial Services 12 weeks

OpenShift Upgrade Program and Workload Transition Planning

Planned and executed a structured OpenShift upgrade program for a financial-services environment where platform downtime, workload compatibility, and release coordination had to be managed through a repeatable readiness and rollout process rather than ad hoc change windows.

OpenShiftRed Hat ACMHelmGitLab CIAnsiblePrometheuskubeconformoc
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ACM-Managed Upgrade Flow

OPENSHIFT UPGRADE PROGRAM — ACM-MANAGED EXECUTION FLOW READINESS Upgrade Matrix ClusterVersion · channels · owners · windows Manifest Prechecks helm lint · helm template · kubeconform Lower-Env Replay server-side validation + representative deploys Go / No-Go Inputs blockers cleared workload owners aligned CHANGE WINDOW Ansible Pre-Flight operators · nodes · certs · routes Canary Cluster Upgrade OpenShift update applied to one cluster first Post-Upgrade Validation operators · alerts · routes · Prometheus targets GitLab Release Gate approve next wave or pause / rollback runbook-driven decision point ACM ROLLOUT WAVES Canary single managed cluster Wave 1 first cluster group Wave 2 remaining clusters Compatibility Confirmation on Upgraded Cluster ingress classes · PVCs · NetworkPolicy · ServiceMonitor · security context Outcome canary-first approval path repeatable wave rollout with pause conditions go gate after canary approve wave

Alternative Multi-Cluster Process Without ACM

MULTI-CLUSTER OPENSHIFT UPGRADE — PROCESS WITHOUT ACM INVENTORY + READINESS Cluster Inventory YAML cluster name · owner · wave · window Manifest Validation helm lint · template · kubeconform Non-Prod Replay server-side checks on upgraded test cluster Wave Definition canary -> wave 1 -> wave 2 pause / rollback rules PIPELINE ORCHESTRATION GitLab CI Pipeline wave parameter selects target clusters Ansible Pre-Flight nodes · operators · certs · route checks Upgrade Playbook loop over clusters in selected wave Post-Upgrade Checks alerts · targets · workload smoke tests manual approval between waves TARGET CLUSTER WAVES Canary one lower-risk cluster Wave 1 first approved set Wave 2 remaining clusters Per-Cluster Execution upgrade one cluster at a time inside the selected wave Outcome multi-cluster upgrades without ACM inventory-driven but still gated and repeatable wave file run wave manual approval

Technical Implementation

  • Built an upgrade readiness matrix from oc adm upgrade output, ClusterVersion history, operator channel versions, and ACM inventory data so each cluster had explicit blockers, prerequisite actions, workload owners, and approved maintenance windows before any upgrade was scheduled.
  • Validated application compatibility in two stages: first by using helm lint, helm template, and kubeconform against the target Kubernetes API version to catch rendering and schema problems early, then by replaying representative deployments and server-side validation checks against upgraded lower-environment or canary clusters to confirm the new platform would still accept and run the workloads correctly.
  • Sequenced the rollout through ACM-managed cluster groups and GitLab CI release gates so a single canary cluster was upgraded first, post-upgrade health checks were reviewed, and only then was the next cluster wave approved for execution.
  • Automated pre-flight and post-upgrade checks with Ansible and oc commands for node readiness, degraded operators, route health, certificate expiry, alert noise, and Prometheus target availability, with rollback and pause conditions documented directly in the runbooks used during the change windows.

Client Delivery & Handover

The work was carried out with the client platform team and application owners through readiness reviews, rehearsal walkthroughs, and controlled change-window planning. Rather than handing over a static recommendation, the engagement produced reusable upgrade checklists, rollback guidance, workload validation procedures, and release-governance documentation. Training sessions were run for platform engineers and support leads so the client could repeat the upgrade model for later cluster lifecycle events without rebuilding the process from scratch.

Outcome

The upgrade process became more predictable and less dependent on heroics, with better visibility into workload readiness, clearer ownership during change windows, and a controlled rollout model the client could reuse for later OpenShift lifecycle events.

Project Snapshot

Category

Kubernetes & OpenShift

Sector

Financial Services

Duration

12 weeks

Next Step

If this project is close to the work your team is planning, Ideamics can discuss comparable architectural decisions, delivery sequencing, and implementation tradeoffs in more detail.

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